Many systems for providing recommendations utilize collaborative filtering, which is a process for filtering for information or patterns using techniques involving collaboration among multiple data points or sources. That is, recommendations are typically made according to user-based collaborative filtering, where items are recommended according to items purchased by similar users. Recommendations are also presently made by systems using item-based collaborative filtering (i.e., the cross-selling of items). Some present systems provide search-based collaborative filtering or content-based collaborative filtering.
Present systems for recommending items are typically skewed, so that popular items are more frequently recommended. Moreover, present systems do not account for direction in item consumption, where, for example, users are more likely to consume item B after item A, but less likely to consume item A after item B.